Where and What: Reasoning Dynamic and Implicit Preferences in Situated Conversational Recommendation (2026.acl-long)
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| Challenge: | Situated conversational recommendation (SCR) uses visual scenes grounded in specific environments and natural language dialogue to deliver contextually appropriate recommendations. |
| Approach: | They propose a framework that integrates scene transition estimation and Bayesian inverse inference to provide contextually appropriate recommendations. |
| Outcome: | The proposed framework achieves superiority over baselines on two representative benchmarks on dynamic scene transitions and implicit user intents. |
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| Challenge: | Existing systems that explore user preference through conversational interactions do not exploit the context and knowledge to make accurate recommendations. |
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Pearl: A Review-driven Persona-Knowledge Grounded Conversational Recommendation Dataset (2024.findings-acl)
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Minjin Kim, Minju Kim, Hana Kim, Beong-woo Kwak, SeongKu Kang, Youngjae Yu, Jinyoung Yeo, Dongha Lee
| Challenge: | Existing datasets for conversational recommender systems lack specific user preferences and explanations for recommendations . current datasets lack specific preferences, hindering high-quality recommendations despite advances in large language models . |
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| Challenge: | Existing systems that update user preferences via asking relevant questions are unable to dynamically maintain and reason over their knowledge for current (and possibly future) recommendations. |
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| Challenge: | Existing evaluation protocols for large language models (LLMs) are inadequate for conversational recommender systems. |
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A Textual Dataset for Situated Proactive Response Selection (2023.acl-long)
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Beyond Static Profiles: Capturing the Fluidity of User Preferences in Diverse Scenarios (2026.findings-acl)
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| Challenge: | Existing approaches to personalize Large Language Models often default to homogeneous behaviors . preferences can shift, and conflict, depending on context, authors argue . |
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